Acute kidney injury (AKI) is an adverse event that carries significant morbidity. Given that interventions after AKI occurrence have poor performance, there is substantial interest in prediction of ...AKI prior to its diagnosis. However, integration of real-time prognostic modeling into the electronic health record (EHR) has been challenging, as complex models increase the risk of error and complicate deployment. Our goal in this study was to create an implementable predictive model to accurately predict AKI in hospitalized patients and could be easily integrated within an existing EHR system.
We performed a retrospective analysis looking at data of 169,859 hospitalized adults admitted to one of three study hospitals in the United States (in New Haven and Bridgeport, Connecticut) from December 2012 to February 2016. Demographics, medical comorbidities, hospital procedures, medications, and laboratory data were used to develop a model to predict AKI within 24 hours of a given observation. Outcomes of AKI severity, requirement for renal replacement therapy, and mortality were also measured and predicted. Models were trained using discrete-time logistic regression in a subset of Hospital 1, internally validated in the remainder of Hospital 1, and externally validated in Hospital 2 and Hospital 3. Model performance was assessed via the area under the receiver-operator characteristic (ROC) curve (AUC). The training set cohort contained 60,701 patients, and the internal validation set contained 30,599 patients. External validation data sets contained 43,534 and 35,025 patients. Patients in the overall cohort were generally older (median age ranging from 61 to 68 across hospitals); 44%-49% were male, 16%-20% were black, and 23%-29% were admitted to surgical wards. In the training set and external validation set, 19.1% and 18.9% of patients, respectively, developed AKI. The full model, including all covariates, had good ability to predict imminent AKI for the validation set, sustained AKI, dialysis, and death with AUCs of 0.74 (95% CI 0.73-0.74), 0.77 (95% CI 0.76-0.78), 0.79 (95% CI 0.73-0.85), and 0.69 (95% CI 0.67-0.72), respectively. A simple model using only readily available, time-updated laboratory values had very similar predictive performance to the complete model. The main limitation of this study is that it is observational in nature; thus, we are unable to conclude a causal relationship between covariates and AKI and do not provide an optimal treatment strategy for those predicted to develop AKI.
In this study, we observed that a simple model using readily available laboratory data could be developed to predict imminent AKI with good discrimination. This model may lend itself well to integration into the EHR without sacrificing the performance seen in more complex models.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
AbstractObjectiveTo determine whether electronic health record alerts for acute kidney injury would improve patient outcomes of mortality, dialysis, and progression of acute kidney ...injury.DesignDouble blinded, multicenter, parallel, randomized controlled trial.SettingSix hospitals (four teaching and two non-teaching) in the Yale New Haven Health System in Connecticut and Rhode Island, US, ranging from small community hospitals to large tertiary care centers.Participants6030 adult inpatients with acute kidney injury, as defined by the Kidney Disease: Improving Global Outcomes (KDIGO) creatinine criteria.InterventionsAn electronic health record based “pop-up” alert for acute kidney injury with an associated acute kidney injury order set upon provider opening of the patient’s medical record.Main outcome measuresA composite of progression of acute kidney injury, receipt of dialysis, or death within 14 days of randomization. Prespecified secondary outcomes included outcomes at each hospital and frequency of various care practices for acute kidney injury.Results6030 patients were randomized over 22 months. The primary outcome occurred in 653 (21.3%) of 3059 patients with an alert and in 622 (20.9%) of 2971 patients receiving usual care (relative risk 1.02, 95% confidence interval 0.93 to 1.13, P=0.67). Analysis by each hospital showed worse outcomes in the two non-teaching hospitals (n=765, 13%), where alerts were associated with a higher risk of the primary outcome (relative risk 1.49, 95% confidence interval 1.12 to 1.98, P=0.006). More deaths occurred at these centers (15.6% in the alert group v 8.6% in the usual care group, P=0.003). Certain acute kidney injury care practices were increased in the alert group but did not appear to mediate these outcomes.ConclusionsAlerts did not reduce the risk of our primary outcome among patients in hospital with acute kidney injury. The heterogeneity of effect across clinical centers should lead to a re-evaluation of existing alerting systems for acute kidney injury.Trial registrationClinicalTrials.gov NCT02753751.
Purpose
By assessing longitudinal associations between COVID‐19 census burdens and hospital characteristics, such as bed size and critical access status, we can explore whether pandemic‐era hospital ...quality benchmarking requires risk‐adjustment or stratification for hospital‐level characteristics.
Methods
We used hospital‐level data from the US Department of Health and Human Services including weekly total hospital and COVID‐19 censuses from August 2020 to August 2023 and the 2021 American Hospital Association survey. We calculated weekly percentages of total adult hospital beds containing COVID‐19 patients. We then calculated the number of weeks each hospital spent at Extreme (≥20% of beds occupied by COVID‐19 patients), High (10%–19%), Moderate (5%–9%), and Low (<5%) COVID‐19 stress. We assessed longitudinal hospital‐level COVID‐19 stress, stratified by 15 hospital characteristics including joint commission accreditation, bed size, teaching status, critical access hospital status, and core‐based statistical area (CBSA) rurality.
Findings
Among n = 2582 US hospitals, the median(IQR) weekly percentage of hospital capacity occupied by COVID‐19 patients was 6.7%(3.6%–13.0%). 80,268/213,383 (38%) hospital‐weeks experienced Low COVID‐19 census stress, 28% Moderate stress, 22% High stress, and 12% Extreme stress. COVID‐19 census burdens were similar across most hospital characteristics, but were significantly greater for critical access hospitals.
Conclusions
US hospitals experienced similar COVID‐19 census burdens across multiple institutional characteristics. Evidence‐based inclusion of pandemic‐era outcomes in hospital quality reporting may not require significant hospital‐level risk‐adjustment or stratification, with the exception of rural or critical access hospitals, which experienced differentially greater COVID‐19 census burdens and may merit hospital‐level risk‐adjustment considerations.
Acute kidney injury is common among hospitalized individuals, particularly those exposed to certain medications, and is associated with substantial morbidity and mortality. In a pragmatic, ...open-label, National Institutes of Health-funded, parallel group randomized controlled trial (clinicaltrials.gov NCT02771977), we investigate whether an automated clinical decision support system affects discontinuation rates of potentially nephrotoxic medications and improves outcomes in patients with AKI. Participants included 5060 hospitalized adults with AKI and an active order for any of three classes of medications of interest: non-steroidal anti-inflammatory drugs, renin-angiotensin-aldosterone system inhibitors, or proton pump inhibitors. Within 24 hours of randomization, a medication of interest was discontinued in 61.1% of the alert group versus 55.9% of the usual care group (relative risk 1.08, 1.04 - 1.14, p = 0.0003). The primary outcome - a composite of progression of acute kidney injury, dialysis, or death within 14 days - occurred in 585 (23.1%) of individuals in the alert group and 639 (25.3%) of patients in the usual care group (RR 0.92, 0.83 - 1.01, p = 0.09). Trial Registration Clinicaltrials.gov NCT02771977.
Primary hepatic neuroendocrine carcinoma (PHNEC) is extremely rare. The diagnosis of PHNEC remains challenging-partly due to its rarity, and partly due to its lack of unique clinical features. ...Available treatment options for PHNEC include surgical resection of the liver tumor(s), radiotherapy, liver transplant, transcatheter arterial chemoembolization (TACE), and administration of somatostatin analogues.
We report two male PHNEC cases and discuss the diagnosis and treatment options. Both cases presented with abdominal pain; case two also presented with symptoms of jaundice. The initial diagnosis for both cases was poorly differentiated grade 3 small-cell neuroendocrine carcinoma, based on imaging characteristics and the pathology of liver biopsies. Final diagnoses of PHNEC were arrived at by ruling out non-hepatic origins. Case one presented with a large tumor in the right liver lobe, and the patient was treated with TACE. Case two presented with tumors in both liver lobes, invasions into the left branch of hepatic portal vein, and metastasis in the hepatic hilar lymph node. This patient was ineligible for TACE and was allergic to the somatostatin analogue octreotide. This limited treatment options to supportive therapies such as albumin supplementation for liver protection. Patient one and two died at 61 and 109 days, respectively, following initial hospital admission.
We diagnosed both cases with poorly differentiated grade 3 small-cell PHNEC through imaging characteristics, immunohistochemical staining of liver biopsies, and examinations to eliminate non-hepatic origins. Neither TACE nor liver protection appeared to significantly extend survival time of the two patients, suggesting these treatments may be inadequate to improve survival of patients with poorly differentiated grade 3 small-cell PHNEC. The prognosis of poorly differentiated grade 3 small-cell PHNEC is poor due to limited and ineffective treatment options.
Reply by Authors Lyon, Timothy D.; Ugwuowo, Ugochukwu C.; Pollock, Benjamin D.
The Journal of urology,
03/2024, Letnik:
211, Številka:
3
Journal Article
OBJECTIVES/SPECIFIC AIMS: Cancer related pain presents a significant risk for opioid abuse among cancer survivors and contributes to the current opioid crisis. Nearly 90% of breast cancer patients ...have been reported to have cancer-related pain requiring treatment. Opioids, in combination with NSAIDs, have been widely used for pain management in this population despite the risk of abuse. Long-term NSAID use due to their antineoplastic and neuroprotective effects may offer additional protective effects against opioid abuse. Here, we assess the relationship between NSAID use and opioid abuse among breast cancer patients. METHODS/STUDY POPULATION: Using ICD-9-CM codes, we identified and selected women aged >18 years with breast cancer from the National Inpatient Sample (NIS). Our primary predictor was a history of long-term NSAID use. Opioid abuse was the primary outcome of interest. Secondary outcomes were inpatient mortality and length of stay. Multivariable regression models were employed in assessing the association between predictors and outcomes while adjusting for relevant covariates. RESULTS/ANTICIPATED RESULTS: Among 170,644 women with breast cancer, 7,838 (4.6%) reported a history of long-term NSAID use. Patients with a history of long-term NSAID use had lower odds of opioid abuse (aOR 0.53; 95% CI 0.32-0.88) and in-hospital mortality (aOR 0.52; 95% CI 0.45-0.60) and were likely to have shorter hospital stay (7.12 vs. 8.11 days) compared to women with no history of long-term NSAID use. DISCUSSION/SIGNIFICANCE OF IMPACT: Long-term NSAID use may offer a protective effect against opioid abuse and improve in-hospital outcomes translating to better quality of life and healthcare utilization indices among breast cancer patients.
IMPORTANCE: The US is currently an epicenter of the coronavirus disease 2019 (COVID-19) pandemic, yet few national data are available on patient characteristics, treatment, and outcomes of critical ...illness from COVID-19. OBJECTIVES: To assess factors associated with death and to examine interhospital variation in treatment and outcomes for patients with COVID-19. DESIGN, SETTING, AND PARTICIPANTS: This multicenter cohort study assessed 2215 adults with laboratory-confirmed COVID-19 who were admitted to intensive care units (ICUs) at 65 hospitals across the US from March 4 to April 4, 2020. EXPOSURES: Patient-level data, including demographics, comorbidities, and organ dysfunction, and hospital characteristics, including number of ICU beds. MAIN OUTCOMES AND MEASURES: The primary outcome was 28-day in-hospital mortality. Multilevel logistic regression was used to evaluate factors associated with death and to examine interhospital variation in treatment and outcomes. RESULTS: A total of 2215 patients (mean SD age, 60.5 14.5 years; 1436 64.8% male; 1738 78.5% with at least 1 chronic comorbidity) were included in the study. At 28 days after ICU admission, 784 patients (35.4%) had died, 824 (37.2%) were discharged, and 607 (27.4%) remained hospitalized. At the end of study follow-up (median, 16 days; interquartile range, 8-28 days), 875 patients (39.5%) had died, 1203 (54.3%) were discharged, and 137 (6.2%) remained hospitalized. Factors independently associated with death included older age (≥80 vs <40 years of age: odds ratio OR, 11.15; 95% CI, 6.19-20.06), male sex (OR, 1.50; 95% CI, 1.19-1.90), higher body mass index (≥40 vs <25: OR, 1.51; 95% CI, 1.01-2.25), coronary artery disease (OR, 1.47; 95% CI, 1.07-2.02), active cancer (OR, 2.15; 95% CI, 1.35-3.43), and the presence of hypoxemia (Pao2:Fio2<100 vs ≥300 mm Hg: OR, 2.94; 95% CI, 2.11-4.08), liver dysfunction (liver Sequential Organ Failure Assessment score of 2-4 vs 0: OR, 2.61; 95% CI, 1.30–5.25), and kidney dysfunction (renal Sequential Organ Failure Assessment score of 4 vs 0: OR, 2.43; 95% CI, 1.46–4.05) at ICU admission. Patients admitted to hospitals with fewer ICU beds had a higher risk of death (<50 vs ≥100 ICU beds: OR, 3.28; 95% CI, 2.16-4.99). Hospitals varied considerably in the risk-adjusted proportion of patients who died (range, 6.6%-80.8%) and in the percentage of patients who received hydroxychloroquine, tocilizumab, and other treatments and supportive therapies. CONCLUSIONS AND RELEVANCE: This study identified demographic, clinical, and hospital-level risk factors that may be associated with death in critically ill patients with COVID-19 and can facilitate the identification of medications and supportive therapies to improve outcomes.
AKI is a common sequela of coronavirus disease 2019 (COVID-19). However, few studies have focused on AKI treated with RRT (AKI-RRT).
We conducted a multicenter cohort study of 3099 critically ill ...adults with COVID-19 admitted to intensive care units (ICUs) at 67 hospitals across the United States. We used multivariable logistic regression to identify patient-and hospital-level risk factors for AKI-RRT and to examine risk factors for 28-day mortality among such patients.
A total of 637 of 3099 patients (20.6%) developed AKI-RRT within 14 days of ICU admission, 350 of whom (54.9%) died within 28 days of ICU admission. Patient-level risk factors for AKI-RRT included CKD, men, non-White race, hypertension, diabetes mellitus, higher body mass index, higher d-dimer, and greater severity of hypoxemia on ICU admission. Predictors of 28-day mortality in patients with AKI-RRT were older age, severe oliguria, and admission to a hospital with fewer ICU beds or one with greater regional density of COVID-19. At the end of a median follow-up of 17 days (range, 1-123 days), 403 of the 637 patients (63.3%) with AKI-RRT had died, 216 (33.9%) were discharged, and 18 (2.8%) remained hospitalized. Of the 216 patients discharged, 73 (33.8%) remained RRT dependent at discharge, and 39 (18.1%) remained RRT dependent 60 days after ICU admission.
AKI-RRT is common among critically ill patients with COVID-19 and is associated with a hospital mortality rate of >60%. Among those who survive to discharge, one in three still depends on RRT at discharge, and one in six remains RRT dependent 60 days after ICU admission.
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